| Autor: |
Shuihai Dou, Yanlin Liu, Yanping Du, Zhaohua Wang, Xiaomei Jia |
| Jazyk: |
angličtina |
| Rok vydání: |
2023 |
| Předmět: |
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| Zdroj: |
International Journal of Computational Intelligence Systems, Vol 16, Iss 1, Pp 1-16 (2023) |
| Druh dokumentu: |
article |
| ISSN: |
1875-6883 |
| DOI: |
10.1007/s44196-023-00301-x |
| Popis: |
Abstract Aiming at the nonlinear and non-stationarity of gearbox fault signals and the confusion among different fault categories, a gear fault diagnosis method combining variational mode decomposition, reconstruction and ResNeXt is proposed in this paper. In this paper, parameter K of VMD is determined according to the changing trend of sample entropy (SE), K modal components are obtained after decomposition, and the effective modal components are extracted and reconstructed according to Pearson autocorrelation coefficient, so as to remove redundant information from the original signal. Then the reconstructed signal is transformed by time–frequency and output two-dimensional time–frequency information, which is used as the input of ResNeXt model to extract the characteristics of different faults. Moreover, the model performance is improved by changing the learning rate decline rate, and a fault diagnosis model with high precision and good stability is established. |
| Databáze: |
Directory of Open Access Journals |
| Externí odkaz: |
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